Show HN: Signet – Autonomous wildfire tracking from satellite and weather data | Hacker News Reader

Show HN: Signet – Autonomous wildfire tracking from satellite and weather data

signet.watch

123 pointsmapldx31 comments

I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking.

All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats.

Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment.

It also records time-bounded predictions and scores them against later data, so the system is making falsifiable claims instead of narrating after the fact. The current prediction metrics are visible on the site even though the sample is still small.

It's already opening incidents from raw satellite detections and matching some to official NIFC reporting. But false positives, detection latency, and incident matching can still be rough.

I'd especially welcome criticism on: where should this be more deterministic instead of LLM-driven? And is this kind of autonomous monitoring actually useful, or just noisier than doing it by hand?

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We do some similar work with hotspot analysis but (as a Canadian company) are more focused on Canadian data where the government already does a fair bit of false positive detection and filtering. It generally gives pretty clean data and we can scrub historical data over time like this: https://imgur.com/a/gCJGzqd

The dataset includes US coverage but it's not filtered the same way and FAR more noisy, so I appreciate efforts like this. We haven't got there yet but if you were looking for something deterministic and automatable the Canadian gov's process is potentially worth learning about.

They also produce perimeter estimates based on the hotspots which we can extract and put into a physics-based fire growth model like Prometheus or FARSITE to estimate future fire behaviour based on forecasted weather. This gives very actionable and deterministic estimates of future fire behaviour. We also have worked on a risk model that determines the likelihood of that future fire growth interacting with various assets on the landscape (urban interface areas, power lines, fuel pipelines, forest inventory, etc) and calls out high risk areas. One thing we've been wondering if where LLMs fit into any of this (if at all) so appreciate seeing what others are doing.

Thanks, this is really helpful. That filtering/perimeter pipeline is exactly the kind of deterministic path I'm interested in learning from, especially for pushing more of the false-positive reduction upstream before the model gets involved at all.

My take so far is that models seem most useful in the contextual triage step and in synthesizing multiple sources into a structured assessment. But most of the system around that is and should be deterministic.

The physics-based approach you're describing makes a lot of sense to me for spread prediction - different tool for a different part of the problem.

If there's a public writeup on the filtering process you'd recommend, I'd love to take a look.

Happy to help. This is the official methods description for the Canadian gov's FM3 data, it's probably the best place to start although the details are mostly covered in much longer publications that require some digging: https://cwfis.cfs.nrcan.gc.ca/background/dsm/fm3

The false positive problem is where I'd push hardest on determinism. In change detection work with Sentinel-2, the most damaging false positives came from seasonal artifacts - snow, agricultural bare soil, cloud shadows - that a well-tuned spectral mask catches reliably before any intelligence layer sees them. SCL cloud masking + NDVI thresholds eliminated a whole class of noise that an LLM would otherwise have to reason its way around.

The principle that worked: anything with a known spectral or geometric signature should be filtered deterministically upstream. Reserve the model for the genuinely ambiguous cases where multiple signals conflict. You probably already know this intuitively from your "most of the system is deterministic plumbing" framing - in my experience the filter boundary can be pushed further upstream than you'd expect.

Super interesting thing to pursue. Will be neat to see where you go with this.

Small nit: don't name incidents until you have the official name. It could cause confusion once it is named.

Instead, something like "New detection near Colusa" or "New incident 10 mi NW of Colusa".

Good call. The system does try to match to official reporting and update when it finds one, but the working names in the meantime could definitely cause confusion.

Probably another case where that should be deterministic instead of model-generated. Thanks.

You might be interested in https://research.google/blog/real-time-tracking-of-wildfire-...

Thanks, looks like a good model reference. Will give it a read.

Interesting. I think there are other services doing the same thing including one linked by another commenter.

When checking the Evidence tab for data that supports the conclusion that there could be a fire in progress I found that it could be improved by excluding the evidence posts for all the mapped fire locations except the one that the user clicked. Presently, if you click that Evidence tab you get a roll of links to posts or mentions or whatever for every fire. I believe that a user would most appreciate data that pertains to the fire they are trying to monitor.

I am not a fan of grey text. It does not improve site navigability or usability and it can get lost in screen glare unless bold grey text is used. It would still be grey text though and I am still not a fan. Perhaps shades of blue or yellow to contrast with the black bar.

Example in case you are thinking of modifying the page - Your top frame has the ap name "SIGNET" in white capitals. Right next to that is an orange dot, probably to signify that something is happening or that the site is "LIVE". Notice that "LIVE" is not only in grey text, beside an orange dot which will be the eyeball magnet, but it is also a smaller font than the ap name "SIGNET".

From my perspective, the site would be improved by changing grey text to a more contrasting color and asking the question - "What information should be the most important topic on this page?" In that way you can optimize it for your users.

Before posting this comment I went back to check that the points I hoped to make were valid points. It turns out that not all "Evidence" links have evidence for every fire on the map. I randomly chose the Custer County Incident when I checked that and found all sorts of stuff pertaining to Texas fires. Perhaps this is not a huge problem for you to solve. I checked the Rapides Parish Incident in Lousyana and it only has data about that event.

Maybe some cleaning of links is in order.

Fair points, I leaned a little hard into the ops aesthetic. Grey text might not be doing anyone any favors.

On the Evidence tab, I agree that it should be incident-specific to be useful on its own. Right now the model scopes what evidence gets attached, so probably a case where that should be deterministic instead.

Good catch. Thanks.

I did the similar thing on https://www.crimewatches.com/, the only pain point is how to aggregate sex offenders' profiles properly across US, too much legal risk there plus labor intensive manual work, you cannot pull the data out easily, CF CAPTCHA gated and short lived token, but will have huge impact on local communities.

Please add more of the world, and it would be great to see some of the imaging data overlayed to visually see where the fire is and scale

Interesting project. Combining satellite detections with weather data seems really powerful.

Thanks!

the Go choice makes a lot of sense for this. i've been wiring up government data sources for a different project and honestly the format inconsistency between agencies is always the real headache, not the actual processing

curious about the 23 tools though -- are those all invoked through one Gemini orchestration pass or is there a routing layer picking which subset to call per detection? feels like that'd stack up fast latency-wise

Not all 23 get invoked in one pass. The system runs 4 different types of cycles, each with its own Gemini call, and within a cycle the model picks a subset of tools based on the context rather than fanning out to everything.

Over the last week, the median ends up being about 6 tool calls across 4 distinct tools per cycle.

Latency-wise, median completed cycle time is about 37s overall. The heavy path is FIRMS: about 135s median / 265s p90 over the same window.

It runs asynchronously in the background, so the web UI isn’t blocked on a cycle finishing, though cycle latency still affects how quickly new detections get enriched.

This is interesting. Are you taking in public data sets in order to have your data accuracy or is the data set your using the proprietary advantaged here?

I’m confused, don’t existing systems like GLFF already incorporate this type of data?

Yes but they do it in a structured way rather than this "autonomous" way where "autonomous" means that no effort has been put into seeing if it works right.

the graphically slick intro suggests this is something that could appeal to "investors" or similar ...

I did lean hard into the presentation, but what I'm actually trying to test here is the monitoring loop and whether it's useful.

How do you even code this, really nice.

have you shared this with the WatchDuty folks?

Not yet.

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